Optimal Interplanetary Orbital Transfers via Electrical Engines View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2005-12

AUTHORS

A. Miele, T. Wang, P. N. Williams

ABSTRACT

The Hohmann transfer theory, developed in the 19th century, is the kernel of orbital transfer with minimum propellant mass by means of chemical engines. The success of the Deep Space 1 spacecraft has paved the way toward using advanced electrical engines in space. While chemical engines are characterized by high thrust and low specific impulse, electrical engines are characterized by low thrust and hight specific impulse. In this paper, we focus on four issues of optimal interplanetary transfer for a spacecraft powered by an electrical engine controlled via the thrust direction and thrust setting: (a) trajectories of compromise between transfer time and propellant mass, (b) trajectories of minimum time, (c) trajectories of minimum propellant mass, and (d) relations with the Hohmann transfer trajectory. The resulting fundamental properties are as follows:(a) Flight Time/Propellant Mass Compromise. For interplanetary orbital transfer (orbital period of order year), an important objective of trajectory optimization is a compromise between flight time and propellant mass. The resulting trajectories have a three-subarc thrust profile: the first and third subarcs are characterized by maximum thrust; the second subarc is characterized by zero thrust (coasting flight); for the first subarc, the normal component of the thrust is opposite to that of the third subarc. When the compromise factor shifts from transfer time (C=0) toward propellant mass (C=1), the average magnitude of the thrust direction for the first and third subarcs decreases, while the flight time of the second subarc (coasting) increases; this results into propellant mass decrease and flight time increase.(b) Minimum Time. The minimum transfer time trajectory is achieved when the compromise factor is totally shifted toward the transfer time (C=0). The resulting trajectory is characterized by a two-subarc thrust profile. In both subarcs, maximum thrust setting is employed and the thrust direction is transversal to the velocity direction. In the first subarc, the normal component of the thrust vector is directed upward for ascending transfer and downward for descending transfer. In the second subarc, the normal component of the thrust vector is directed downward for ascending transfer and upward for descending transfer.(c) Minimum Propellant Mass. The minimum propellant mass trajectory is achieved when the compromise factor is totally shifted toward propellant mass (C=1). The resulting trajectory is characterized by a three-subarc (bang-zero-bang) thrust profile, with the thrust direction tangent to the flight path at all times.(d) Relations with the Hohmann Transfer. The Hohmann transfer trajectory can be regarded as the asymptotic limit of the minimum propellant mass trajectory as the thrust magnitude tends to infinity. The Hohmann transfer trajectory provides lower bounds for the propellant mass, flight time, and phase angle travel of the minimum propellant mass trajectory.The above properties are verified computationally for two cases (a) ascending transfer from Earth orbit to Mars orbit; and (b) descending transfer from Earth orbit to Venus orbit. The results are obtained using the sequential gradient- restoration algorithm in either single-subarc form or multiple-subarc form. More... »

PAGES

605-625

References to SciGraph publications

  • 2003-01. Multiple-Subarc Gradient-Restoration Algorithm, Part 2: Application to a Multistage Launch Vehicle Design in JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS
  • 2003-01. Multiple-Subarc Gradient-Restoration Algorithm, Part 1: Algorithm Structure in JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS
  • 2000-06. Optimal Free-Return Trajectories for Moon Missions and Mars Missions in THE JOURNAL OF THE ASTRONAUTICAL SCIENCES
  • 2004-11. Reflections on the Hohmann Transfer in JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS
  • 1970-04. Sequential gradient-restoration algorithm for optimal control problems in JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10957-005-7506-9

    DOI

    http://dx.doi.org/10.1007/s10957-005-7506-9

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1033125658


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/09", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Engineering", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0901", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Aerospace Engineering", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Aero-Astronautics Group, Rice University, Houston, Texas", 
              "id": "http://www.grid.ac/institutes/grid.21940.3e", 
              "name": [
                "Aero-Astronautics Group, Rice University, Houston, Texas"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Miele", 
            "givenName": "A.", 
            "id": "sg:person.015552732657.49", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015552732657.49"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Aero-Astronautics Group, Rice University, Houston, Texas", 
              "id": "http://www.grid.ac/institutes/grid.21940.3e", 
              "name": [
                "Aero-Astronautics Group, Rice University, Houston, Texas"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Wang", 
            "givenName": "T.", 
            "id": "sg:person.014414570607.44", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014414570607.44"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Aero-Astronautics Group, Rice University, Houston, Texas", 
              "id": "http://www.grid.ac/institutes/grid.21940.3e", 
              "name": [
                "Aero-Astronautics Group, Rice University, Houston, Texas"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Williams", 
            "givenName": "P. N.", 
            "id": "sg:person.014665410617.05", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014665410617.05"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1023/a:1022154001343", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020008711", 
              "https://doi.org/10.1023/a:1022154001343"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10957-004-5147-z", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001300307", 
              "https://doi.org/10.1007/s10957-004-5147-z"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00927913", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1010952160", 
              "https://doi.org/10.1007/bf00927913"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf03546276", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1130303714", 
              "https://doi.org/10.1007/bf03546276"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/a:1022114117273", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022677003", 
              "https://doi.org/10.1023/a:1022114117273"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2005-12", 
        "datePublishedReg": "2005-12-01", 
        "description": "The Hohmann transfer theory, developed in the 19th century, is the kernel of orbital transfer with minimum propellant mass by means of chemical engines. The success of the Deep Space 1 spacecraft has paved the way toward using advanced electrical engines in space. While chemical engines are characterized by high thrust and low specific impulse, electrical engines are characterized by low thrust and hight specific impulse. In this paper, we focus on four issues of optimal interplanetary transfer for a spacecraft powered by an electrical engine controlled via the thrust direction and thrust setting: (a) trajectories of compromise between transfer time and propellant mass, (b) trajectories of minimum time, (c) trajectories of minimum propellant mass, and (d) relations with the Hohmann transfer trajectory. The resulting fundamental properties are as follows:(a) Flight Time/Propellant Mass Compromise. For interplanetary orbital transfer (orbital period of order year), an important objective of trajectory optimization is a compromise between flight time and propellant mass. The resulting trajectories have a three-subarc thrust profile: the first and third subarcs are characterized by maximum thrust; the second subarc is characterized by zero thrust (coasting flight); for the first subarc, the normal component of the thrust is opposite to that of the third subarc. When the compromise factor shifts from transfer time (C=0) toward propellant mass (C=1), the average magnitude of the thrust direction for the first and third subarcs decreases, while the flight time of the second subarc (coasting) increases; this results into propellant mass decrease and flight time increase.(b) Minimum Time. The minimum transfer time trajectory is achieved when the compromise factor is totally shifted toward the transfer time (C=0). The resulting trajectory is characterized by a two-subarc thrust profile. In both subarcs, maximum thrust setting is employed and the thrust direction is transversal to the velocity direction. In the first subarc, the normal component of the thrust vector is directed upward for ascending transfer and downward for descending transfer. In the second subarc, the normal component of the thrust vector is directed downward for ascending transfer and upward for descending transfer.(c) Minimum Propellant Mass. The minimum propellant mass trajectory is achieved when the compromise factor is totally shifted toward propellant mass (C=1). The resulting trajectory is characterized by a three-subarc (bang-zero-bang) thrust profile, with the thrust direction tangent to the flight path at all times.(d) Relations with the Hohmann Transfer. The Hohmann transfer trajectory can be regarded as the asymptotic limit of the minimum propellant mass trajectory as the thrust magnitude tends to infinity. The Hohmann transfer trajectory provides lower bounds for the propellant mass, flight time, and phase angle travel of the minimum propellant mass trajectory.The above properties are verified computationally for two cases (a) ascending transfer from Earth orbit to Mars orbit; and (b) descending transfer from Earth orbit to Venus orbit. The results are obtained using the sequential gradient- restoration algorithm in either single-subarc form or multiple-subarc form.", 
        "genre": "article", 
        "id": "sg:pub.10.1007/s10957-005-7506-9", 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1044187", 
            "issn": [
              "0022-3239", 
              "1573-2878"
            ], 
            "name": "Journal of Optimization Theory and Applications", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "3", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "127"
          }
        ], 
        "keywords": [
          "electrical engines", 
          "propellant mass", 
          "orbital transfer", 
          "minimum propellant mass", 
          "specific impulse", 
          "thrust vector", 
          "thrust direction", 
          "sequential gradient-restoration algorithm", 
          "thrust profile", 
          "low specific impulse", 
          "Hohmann transfer", 
          "gradient-restoration algorithm", 
          "chemical engines", 
          "flight time", 
          "first subarc", 
          "normal component", 
          "minimum transfer time", 
          "second subarc", 
          "trajectory optimization", 
          "Earth orbit", 
          "high thrust", 
          "subarcs", 
          "maximum thrust", 
          "asymptotic limit", 
          "minimum time", 
          "lower bounds", 
          "direction tangent", 
          "thrust magnitude", 
          "transfer time", 
          "flight time increases", 
          "low thrust", 
          "Deep Space 1 spacecraft", 
          "velocity direction", 
          "thrust settings", 
          "engine", 
          "fundamental properties", 
          "Venus orbit", 
          "flight path", 
          "interplanetary transfer", 
          "orbit", 
          "transfer theory", 
          "time increases", 
          "factor shifts", 
          "above properties", 
          "Mars orbit", 
          "trajectories", 
          "thrust", 
          "spacecraft", 
          "bounds", 
          "infinity", 
          "transfer", 
          "properties", 
          "compromise factor", 
          "direction", 
          "optimization", 
          "vector", 
          "theory", 
          "mass decrease", 
          "kernel", 
          "average magnitude", 
          "tangent", 
          "space", 
          "algorithm", 
          "magnitude", 
          "important objective", 
          "components", 
          "time", 
          "impulses", 
          "compromise", 
          "profile", 
          "form", 
          "mass", 
          "increase", 
          "path", 
          "limit", 
          "relation", 
          "decrease", 
          "means", 
          "results", 
          "way", 
          "Mass.", 
          "objective", 
          "issues", 
          "factors", 
          "shift", 
          "travel", 
          "setting", 
          "success", 
          "century", 
          "paper"
        ], 
        "name": "Optimal Interplanetary Orbital Transfers via Electrical Engines", 
        "pagination": "605-625", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1033125658"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s10957-005-7506-9"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s10957-005-7506-9", 
          "https://app.dimensions.ai/details/publication/pub.1033125658"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-12-01T06:25", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20221201/entities/gbq_results/article/article_395.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1007/s10957-005-7506-9"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s10957-005-7506-9'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s10957-005-7506-9'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10957-005-7506-9'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10957-005-7506-9'


     

    This table displays all metadata directly associated to this object as RDF triples.

    181 TRIPLES      21 PREDICATES      120 URIs      107 LITERALS      6 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s10957-005-7506-9 schema:about anzsrc-for:09
    2 anzsrc-for:0901
    3 schema:author N14eb59fb17e442e7bb3d5dd0fcc461f4
    4 schema:citation sg:pub.10.1007/bf00927913
    5 sg:pub.10.1007/bf03546276
    6 sg:pub.10.1007/s10957-004-5147-z
    7 sg:pub.10.1023/a:1022114117273
    8 sg:pub.10.1023/a:1022154001343
    9 schema:datePublished 2005-12
    10 schema:datePublishedReg 2005-12-01
    11 schema:description The Hohmann transfer theory, developed in the 19th century, is the kernel of orbital transfer with minimum propellant mass by means of chemical engines. The success of the Deep Space 1 spacecraft has paved the way toward using advanced electrical engines in space. While chemical engines are characterized by high thrust and low specific impulse, electrical engines are characterized by low thrust and hight specific impulse. In this paper, we focus on four issues of optimal interplanetary transfer for a spacecraft powered by an electrical engine controlled via the thrust direction and thrust setting: (a) trajectories of compromise between transfer time and propellant mass, (b) trajectories of minimum time, (c) trajectories of minimum propellant mass, and (d) relations with the Hohmann transfer trajectory. The resulting fundamental properties are as follows:(a) Flight Time/Propellant Mass Compromise. For interplanetary orbital transfer (orbital period of order year), an important objective of trajectory optimization is a compromise between flight time and propellant mass. The resulting trajectories have a three-subarc thrust profile: the first and third subarcs are characterized by maximum thrust; the second subarc is characterized by zero thrust (coasting flight); for the first subarc, the normal component of the thrust is opposite to that of the third subarc. When the compromise factor shifts from transfer time (C=0) toward propellant mass (C=1), the average magnitude of the thrust direction for the first and third subarcs decreases, while the flight time of the second subarc (coasting) increases; this results into propellant mass decrease and flight time increase.(b) Minimum Time. The minimum transfer time trajectory is achieved when the compromise factor is totally shifted toward the transfer time (C=0). The resulting trajectory is characterized by a two-subarc thrust profile. In both subarcs, maximum thrust setting is employed and the thrust direction is transversal to the velocity direction. In the first subarc, the normal component of the thrust vector is directed upward for ascending transfer and downward for descending transfer. In the second subarc, the normal component of the thrust vector is directed downward for ascending transfer and upward for descending transfer.(c) Minimum Propellant Mass. The minimum propellant mass trajectory is achieved when the compromise factor is totally shifted toward propellant mass (C=1). The resulting trajectory is characterized by a three-subarc (bang-zero-bang) thrust profile, with the thrust direction tangent to the flight path at all times.(d) Relations with the Hohmann Transfer. The Hohmann transfer trajectory can be regarded as the asymptotic limit of the minimum propellant mass trajectory as the thrust magnitude tends to infinity. The Hohmann transfer trajectory provides lower bounds for the propellant mass, flight time, and phase angle travel of the minimum propellant mass trajectory.The above properties are verified computationally for two cases (a) ascending transfer from Earth orbit to Mars orbit; and (b) descending transfer from Earth orbit to Venus orbit. The results are obtained using the sequential gradient- restoration algorithm in either single-subarc form or multiple-subarc form.
    12 schema:genre article
    13 schema:isAccessibleForFree false
    14 schema:isPartOf N00ce15b610604102a8cbfafb02119584
    15 N8609b5b352a5421d94ed5bfe917008d0
    16 sg:journal.1044187
    17 schema:keywords Deep Space 1 spacecraft
    18 Earth orbit
    19 Hohmann transfer
    20 Mars orbit
    21 Mass.
    22 Venus orbit
    23 above properties
    24 algorithm
    25 asymptotic limit
    26 average magnitude
    27 bounds
    28 century
    29 chemical engines
    30 components
    31 compromise
    32 compromise factor
    33 decrease
    34 direction
    35 direction tangent
    36 electrical engines
    37 engine
    38 factor shifts
    39 factors
    40 first subarc
    41 flight path
    42 flight time
    43 flight time increases
    44 form
    45 fundamental properties
    46 gradient-restoration algorithm
    47 high thrust
    48 important objective
    49 impulses
    50 increase
    51 infinity
    52 interplanetary transfer
    53 issues
    54 kernel
    55 limit
    56 low specific impulse
    57 low thrust
    58 lower bounds
    59 magnitude
    60 mass
    61 mass decrease
    62 maximum thrust
    63 means
    64 minimum propellant mass
    65 minimum time
    66 minimum transfer time
    67 normal component
    68 objective
    69 optimization
    70 orbit
    71 orbital transfer
    72 paper
    73 path
    74 profile
    75 propellant mass
    76 properties
    77 relation
    78 results
    79 second subarc
    80 sequential gradient-restoration algorithm
    81 setting
    82 shift
    83 space
    84 spacecraft
    85 specific impulse
    86 subarcs
    87 success
    88 tangent
    89 theory
    90 thrust
    91 thrust direction
    92 thrust magnitude
    93 thrust profile
    94 thrust settings
    95 thrust vector
    96 time
    97 time increases
    98 trajectories
    99 trajectory optimization
    100 transfer
    101 transfer theory
    102 transfer time
    103 travel
    104 vector
    105 velocity direction
    106 way
    107 schema:name Optimal Interplanetary Orbital Transfers via Electrical Engines
    108 schema:pagination 605-625
    109 schema:productId Nb71418dc27e643b39f031333de2f4d32
    110 Nf958da6ba9f74ce484be082db0a53c52
    111 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033125658
    112 https://doi.org/10.1007/s10957-005-7506-9
    113 schema:sdDatePublished 2022-12-01T06:25
    114 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    115 schema:sdPublisher N25ec911c2620423491622ad4cb0c56a8
    116 schema:url https://doi.org/10.1007/s10957-005-7506-9
    117 sgo:license sg:explorer/license/
    118 sgo:sdDataset articles
    119 rdf:type schema:ScholarlyArticle
    120 N00ce15b610604102a8cbfafb02119584 schema:volumeNumber 127
    121 rdf:type schema:PublicationVolume
    122 N14eb59fb17e442e7bb3d5dd0fcc461f4 rdf:first sg:person.015552732657.49
    123 rdf:rest N8741e2e2f2384360bb53d4880fedc820
    124 N25ec911c2620423491622ad4cb0c56a8 schema:name Springer Nature - SN SciGraph project
    125 rdf:type schema:Organization
    126 N8609b5b352a5421d94ed5bfe917008d0 schema:issueNumber 3
    127 rdf:type schema:PublicationIssue
    128 N8741e2e2f2384360bb53d4880fedc820 rdf:first sg:person.014414570607.44
    129 rdf:rest Ndcd7c74527984f04b34884541ea7d2dc
    130 Nb71418dc27e643b39f031333de2f4d32 schema:name dimensions_id
    131 schema:value pub.1033125658
    132 rdf:type schema:PropertyValue
    133 Ndcd7c74527984f04b34884541ea7d2dc rdf:first sg:person.014665410617.05
    134 rdf:rest rdf:nil
    135 Nf958da6ba9f74ce484be082db0a53c52 schema:name doi
    136 schema:value 10.1007/s10957-005-7506-9
    137 rdf:type schema:PropertyValue
    138 anzsrc-for:09 schema:inDefinedTermSet anzsrc-for:
    139 schema:name Engineering
    140 rdf:type schema:DefinedTerm
    141 anzsrc-for:0901 schema:inDefinedTermSet anzsrc-for:
    142 schema:name Aerospace Engineering
    143 rdf:type schema:DefinedTerm
    144 sg:journal.1044187 schema:issn 0022-3239
    145 1573-2878
    146 schema:name Journal of Optimization Theory and Applications
    147 schema:publisher Springer Nature
    148 rdf:type schema:Periodical
    149 sg:person.014414570607.44 schema:affiliation grid-institutes:grid.21940.3e
    150 schema:familyName Wang
    151 schema:givenName T.
    152 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014414570607.44
    153 rdf:type schema:Person
    154 sg:person.014665410617.05 schema:affiliation grid-institutes:grid.21940.3e
    155 schema:familyName Williams
    156 schema:givenName P. N.
    157 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014665410617.05
    158 rdf:type schema:Person
    159 sg:person.015552732657.49 schema:affiliation grid-institutes:grid.21940.3e
    160 schema:familyName Miele
    161 schema:givenName A.
    162 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015552732657.49
    163 rdf:type schema:Person
    164 sg:pub.10.1007/bf00927913 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010952160
    165 https://doi.org/10.1007/bf00927913
    166 rdf:type schema:CreativeWork
    167 sg:pub.10.1007/bf03546276 schema:sameAs https://app.dimensions.ai/details/publication/pub.1130303714
    168 https://doi.org/10.1007/bf03546276
    169 rdf:type schema:CreativeWork
    170 sg:pub.10.1007/s10957-004-5147-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1001300307
    171 https://doi.org/10.1007/s10957-004-5147-z
    172 rdf:type schema:CreativeWork
    173 sg:pub.10.1023/a:1022114117273 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022677003
    174 https://doi.org/10.1023/a:1022114117273
    175 rdf:type schema:CreativeWork
    176 sg:pub.10.1023/a:1022154001343 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020008711
    177 https://doi.org/10.1023/a:1022154001343
    178 rdf:type schema:CreativeWork
    179 grid-institutes:grid.21940.3e schema:alternateName Aero-Astronautics Group, Rice University, Houston, Texas
    180 schema:name Aero-Astronautics Group, Rice University, Houston, Texas
    181 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...